data foundation for analytics excellence by tanimura, cathy from okta
TRANSCRIPT
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Data Foundation for Analytics Excellence
Cathy Tanimura
Director of Analytics & Big Data @ Okta
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Agenda
• Intro
• Data Foundation • Finding the Problem(s)
• Getting Started: Proof of Concept
• Picking the Technology
• Building Out: What to Expect
• People Foundation • Building the Team
• Partners and Champions
• Bringing it All Together
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Intro
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Background
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Okta?
“In meteorology, an okta is a unit of measurement used to describe the amount of cloud cover at any given location such as a weather station.
Sky conditions are estimated in terms of how many eighths of the sky are covered in cloud, ranging from 0 oktas (completely clear sky) through to 8 oktas (completely overcast).”
- Wikipedia
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4 Million+
People
10 Million+
Devices
The Enterprise Identity Network
3,000+
Applications
On
Pre
m
Clo
ud
Mo
bile
1,600+ Organizations
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Problems Okta Solves
• User Password Fatigue
• Failure-Prone Manual Provisioning & De-Provisioning Process
• Compliance Visibility: Who Has Access to What?
• Siloed User Directories for Each Application
• Managing Access across an Explosion of Browsers and Devices
• Keeping Application Integrations Up to Date
• Different Administration Models for Different Applications
• Sub-Optimal Utilization, and Lack of Insight into Best Practices
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Focus on the end-user
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Data Foundation
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Data Foundation
• Finding the Problem(s)
•Getting Started: Proof of Concept
•Picking the Technology
•Building Out: What to Expect
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Finding the Problem
• First thing you want to tackle
•Prove value
•Research for long-term infrastructure
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What Makes a Good Problem
•Big business impact: $$’s, time
•Data available
• Someone has tried to tackle
• Engaged business partner
•Clear vision of what will change
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Common “Problems”
•Marketing optimization
•Multi-channel attribution
•User behavior
• Fraud detection
•Recommendations
•Viral / market penetration
•Retention / churn
•Resource allocation
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Finding the Problem
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Finding the Problem
• “Virals” were major growth and retention tool
• How many new users did we attract?
• How many came back?
• How effective was this feature at driving traffic?
• How does play spread from friend to friend?
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Finding the Problem
Activities: • Add directory • Import users • Add apps • Assign users • Rollout plan
Adoption
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Finding the Problem
Why do we care about adoption?
• Happy customers renewals, references, upsell opportunities
Sub-Problems:
• How many customers?
• Does it really affect churn?
• Can we influence?
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Proof of Concept
• Find the data
• Simple, low cost tools
•Build something
•Get feedback
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POC: Find the Data
Social
Cloud Apps
In-house Apps
On-Prem Databases
3rd party
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Finding the Data Example
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POC: Simple, low-cost tools
•What do you already have
•Open-source
• Trials / community editions
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POC: Example Data Infrastructure
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Building
•Define the metrics • Understandable • Measurable • Actionable
•Visualize
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Building the Metric: Example
• At a high level, Adoption = Usage / Entitlement
• What is the best “usage” measure?
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Showing the Metric Matters
• Some outliers, but adoption correlated with renewal
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Get Feedback
• Share
• Listen
•Pay attention to where the data doesn’t fit the “smell test”. At first your clients will have a better sense than you do
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Feedback: Prototype Example
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Pick the Technology
• The fun part (sort of)
• Start with requirements discovered during POC
•Be aware of the market, but not distracted
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Data Store Decisions
Vs.
Vs.
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ETL Decisions
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Front-End Options
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Tips on Selling the Technology
• Educate: what does each piece do (in layman’s terms)
•Present S,M,L cost options
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Data Mining, Modeling, Stats
BI Tools Source Systems
Operational Systems (“Prod”)
Cloud Services
Web Data
External Data
Data Storage ETL / Data Integration
Streaming, Event Processing
“End to End”
Analysis, Viz Data Warehouse
(SQL)
Hadoop Platforms
Point Solutions
Example: Tech & Vendor Landscape
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Example: S,M,L Options
Small
• $0k • 0 extra FTE • Rely on forums,
learn as we go
• Timeline: 12+ Months
Medium
• $100k • 1 FTE • Access to
expertise
• Timeline: 6-9 Months
Large
• $200k • 2 FTE • Access to
expertise
• Timeline: 3 – 6 Months
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Building Out: What to Expect
• It will never go “as expected”
• Time will be more than expected
•$ will be more than expected
Develop the vision up-front, fill in details as you go
Consider Agile development
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Building Out: What to Expect
•Stuff that happens: •People change •New data source •Holidays & vacations • Integrations break •Data quality
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What to Expect
You never “finish” analytics…
Known Knowns Easy stuff
Unknown Knowns
Duh
Unknown Unknowns
Uh-oh
Known Unknowns
Aha!
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People Foundation
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Building the Team
•Who
•When
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Building a Team
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Who
Data Analyst
Focus: • Analysis,
reports, dashboards
Aligned to: • Business Languages: • SQL, R, Excel
Data Scientist
Focus: • Data products • Modeling
Aligned to: • Product
Languages: • R, Python, SQL
Data Engineer
Focus: • Data
infrastructure • Scalability
Aligned to: • Engineering Languages: • Java, Python,
MapReduce
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When to Build the Team
Delphi Analytics, April 1, 2013
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When to Build the Team
• Scale with business
• Infrastructure in place
•Generate demand from clients
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Partners & Champions
• Easily overlooked but key to success
•Partners are your clients • Typically Marketing, Finance, Product,
BizDev
•And the teams you rely on • IT, Engineering, Product
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Partners & Champions
•Champions are execs and people on the ground who can spread the word • Execs want clear and simple messages:
what are the benefits, how much will it cost
• You never know who your other champions are going to be. Don’t miss opportunities to help people out
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Putting It All Together
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Tech Stack - Vision
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What are the Effects?
• Time savings • Time spent collecting & processing data by Customer
Success, Renewals, Product
• Time spent telling anecdotes
• Revenue: • Save at-risk renewals: early awareness tells us where to
intervene
• Upsells: Visibility into usage lets sales people have more timely & informed discussions about upsells
• Focus • On the features that matter (not ones that don’t)
• Take the guesswork out of meetings
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Questions?